A Comparative Performance Evaluation of Swarm Intelligence Techniques
Nowadays Swarm Intelligent based optimizations techniques are becoming popular for solving modern engineering problems. This paper presents a programmable resultant numerical comparative analysis of optimization algorithms namely, Artificial Bee Colony Algorithm (ABCA), Ant Colony Optimization Algorithm (ACOA), Fire-fly Algorithm (FFA) and Particle Swarm Optimization Algorithm (PSOA). Fitness functions are the part of the algorithms to determine the fitness of values. Various generalised fitness functions such as beale, bukin, etc. are programmed for considered algorithms. These fitness functions are used to simulate the considered algorithms and the obtained results are tabulated and compared in this paper.